Exact distributions for stochastic gene expression models with bursting and feedback

Abstract

Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play important roles in controlling noise in gene expression and phenotypic variation. A quantitative understanding of the impact of these factors requires analysis of the corresponding stochastic models. However, for stochastic models of gene expression with feedback and bursting, exact analytical results for protein distributions have not been obtained so far. Here, we analyze a model of gene expression with bursting and feedback regulation and obtain exact results for the corresponding protein steady-state distribution. The results obtained provide new insights into the role of bursting and feedback in noise regulation and optimization. Furthermore, for a specific choice of parameters, the system studied maps on to a two-state biochemical switch driven by a bursty input noise source. The analytical results derived thus provide quantitative insights into diverse cellular processes involving noise in gene expression and biochemical switching.

Accepted in Phys. Rev. Lett

References in corpus (2)

• Analytical distributions for stochastic gene expression
• Exact solution of a model DNA-inversion genetic switch with orientational control

Cited by in corpus (9)

• Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
• Simplification of Markov chains with infinite state space and the mathematical theory of random gene expression bursts
• Emergent Lévy behavior in single-cell stochastic gene expression
• Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback
• Time-dependent solutions for a stochastic model of gene expression with molecule production in the form of a compound Poisson process
• Analytic solution of chemical master equations involving gene switching. I: Representation theory and diagrammatic approach to exact solution
• Exact distributions for stochastic models of gene expression with arbitrary regulation
• Moment Closure Approximations in a Genetic Negative Feedback Circuit
• Quantifying the noise in bursty gene expression under regulation by small RNAs